«The digital doctor will see you now»

Can a computer predict disease and give advice on which treatment is best for you?

Many diseases are caused by complex biological factors that are hard to understand without using extensive computational tools. Doctors and researchers need help from a digital doctor.

– We develop digital models to give a more precise diagnosis and to develop better treatment, says associate professor Marianne Fyhn at the Department of Biosciences at the University of Oslo.

The neuroscientist speaks of schizophrenia, but the same principle also underlies research projects directed towards understanding cancer and Parkinson’s disease.

First, the researchers must gather large amounts of data from a biological system. Based on these data they develop complex mathematical models that describe how the system is connected. If the models are good and represent reality, they should be able to predict how the system will change when something happens.

The researchers can then perform experiments to test if the virtual calculations match the real world.

Communication between brain cells

Fyhn and her colleagues in the DigiBrain project base their work on a worldwide analysis of samples from almost 100 000 schizophrenia patients and an equal number of controls. The results show that 120-150 genetic variants are more often present in the patients compared to the healthy population.

– We want do discover what these genetic variants mean for the communication between the brain cells, and how brain activity is connected to the disease, says Fyhn.

To do this they measure brain activity from the outside of the brain using electrodes placed on a tight-fitting hat. The method is called electroencephalography, abbreviated EEG.

Postdoctoral researcher and participant in DigiBrain Torbjørn Elvsåshagen uses EEG-measurements mostly to examine patients with epilepsy. The technique can also contribute to the research towards understanding brain activity in patients with schizophrenia and bipolar disorder.

– We use EEG in our research to discover disease mechanisms. We don’t know the details of what is different in the brain of people with schizophrenia and bipolar disorder, and many patients experience that their medication does not work, he says.

Scared by a loud noise

At the clinic at Ullevål hospital, Elvsåshagen uses EEG to perform a test called «pre-pulse inhibition».

– All animals react instinctively by startling if they hear a loud noise. In the laboratory, we can measure this response with EEG and sensors that register muscle contractions around the eye, he explains.

The reaction to the loud noise is usually reduced if the test person first hears a lower noise, a so-called pre-pulse. The first sound causes the brain to reduce the reaction to the following loud noise.

By comparing large groups of people the researchers see that schizophrenia patients do not react the same way as health volunteers. People with schizophrenia are less affected by the pre-pulse, and still get startled by the loud noise.

– We also want to explore genetic variants linked to schizophrenia and bipolar disorder affect how brain cells communicate with each other by using EEG-readings and digital models, says Elvsåshagen.

Brain tissue in research

EEG provides a complex view of brain activity, but to fully understand how the brain functions in health and disease, the researchers also need to study the brain tissue itself.

At the University of Bergen (UiB), group leader Charalampos Tzoulis researches brain tissue from patients with Parkinson’s disease. So far, he and his colleagues in the project ParkOme have analysed brain tissue from a total of hundred healthy and sick individuals who have donated their bodies to research.

– We try to «digitise» the brain to discover the causes of Parkinson’s disease, says Tzoulis.

The analyses are expensive and exhaustive, and provide a complete overview of DNA, epigenetic regulation, RNA and protein in the tissue.

– We put all the information together in a database, and use algorithms and machine learning to try to understand how the network is coupled together. We hope to discover how brain tissue from the patients is different from healthy brain tissue, explains Tzoulis.

Unique Norway

The information about the brain tissue can also be put together with other types of data to give new insights. The Norwegian health registries represent a unique opportunity for researchers like Tzoulis.

– We have used the drug registry to discover that a medication for diabetes can reduce the risk of Parkinson’s disease, explains the researcher, who will soon start a new clinical trial based on hypotheses from the digital model.

Clinical studies to test new drugs can also have value after the trial has been completed. At UiO, mathematician Alvaro Köhn-Luque uses data from a study on treatment of breast cancer (NeoAva) to build his models.

In the projects BigInsight and PERCATHE, led by professor Arnoldo Frigessi at the Oslo University Hospital, the goals are to develop advanced mathematical models that can assist in selecting the right treatment for cancer patients.

1000 virtual cancer cells

– We develop virtual copies of a small part of the tumor, and include as much data about the patient and the cancer cells as we can. Then we test giving the same treatment in the computer model as the actual patient got, explains Köhn-Luque.

First, he tests if the digital model behaves similar to how the treatment affected the patient. Then he gives the computer freedom to test a variety of other treatment regimes that might result in a better outcome for the digital patient.

The digital patient represents only a tiny portion of the tumour, and is based on information from about 1000 cells in total. A whole tumour may contain hundreds of millions of cells, both healthy cells and different populations of cancer cells.

The models still have a way to go before they resemble a living patient.

– Our models are a big simplification of reality. Our findings provide new theories and knowledge that we hope to use in the clinic in the future, says Köhn-Luque.

Data determines treatment

At Haukeland University hospital, professor Bjørn Tore Gjertsen has already started using detailed information about the patients’ cancer cells to evaluate treatment options. Gjertsen leads one of the research projects in CCBIO, a Centre of Excellence at UiB.

Gjertsen’s patients have different cancers that affect cells in the blood. For most of the subgroups of patients, targeted therapies do not exist.

– Althoug standard treatment often can slow the disease progression, we don’t know what is happening to the disease during treatment, he says.

Gjertsen and his colleagues test if the analysis method called mass cytometry, together with a drug that blocks the protein ALX, can improve the prognosis for some of the patients.

– We think we have good biomarkers for finding the patients who will respond to the AXL-inhibitor, and we use complex molecular data to predict who might benefit from trying the new drug, he says.

Adjusting treatment based on models

In a clinical trial they have followed 15 patients over time, and tested the dose and tolerance of the drug. Gjertsen has collected data about genetic mutations in the cancer cells, response to treatment, and detailed information about cells from the blood and bone marrow. Since these data follow the patient over time, the complexity increases.

Repeated blood samples allow close monitoring of how the patient responds, and Gjertsen depends on a close collaboration between mathematicians, statisticians and bioinformaticians to analyse and interpret the complex data.

The results are very valuable.

– We have a lot of data that we would very much like to use in the clinic, because we think that it will tell us who is responding much faster. We will no longer treat patients blindly, and this will contribute to give better treatment to the patients. We wish to use complex analyses to discover changes in the patients, and then decide if we should adjust the treatment, says Gjertsen.

Man, statistics and fish

Complex datasets can provide better treatment, and the models are also a gold mine for experiments that aim towards new knowledge in a tumour or in the brains of patients with Parkinson’s disease or schizophrenia.

At Oslo Science Park assistant Professor Camila Esguerra works to explore the consequences of genetic alterations in the brain cells of small fish. Esguerra is one of the partners in DigiBrain, where she collaborates with Fyhn and Elsvåshagen to understand the causes and find novel treatment options for patients with schizophrenia.

– The overall goal of the project is to explore how genetic changes alter how brain cells communicate with each other, she says.

Scaring fish and mice

Esguerra uses zebrafish with defects in one of the genes that is also altered the schizophrenia patients. She uses the same «pre-pulse inhibition» assay as Elvsåshagen employs with his patients.

The collaboration provides important synergy between the disciplines.

– Our fish are not a replacement for experiments with mice or data from humans. It is the combination of models that gives strength to our project, and gives us a better understanding, says Esguerra.

In the project the researchers also develop computer models of brain cells with the same changes, and at the Department og Biosciences, Fyhn uses the «pre-pulse inhibition» assay on mice.

– By changing the activity of the brain cells we can see thaty the mice are more startled. Now we will use the gene editing tool CRISPR to mimic the human gene variants and characterize the effects on brain cells in detail, she says.

The interplay between digital and experimental results will hopefully contribute to better diagnosis and better treatment. In the future, the DigiBrain partners will test and develop new drugs based on their findings.

– Only the beginning

For the abovementioned projects to be successful, the researchers depend on close collaborations between very different disciplines to tackle complex biological processes. This takes patience and a will to collaborate.

The large health projects share their high ambitions for how the digital models will increase the knowledge about diseases and save lives. The commitment is both broad and long term.

– The research goes far beyond just this one project. This is just the beginning, concludes Esguerra.